A method for continuous speech segmentation using HMM

A statistical method of segmentation using a hidden Markov model (HMM) and a Bayesian classifier is described. The main features of this method are the use of feature parameters which are independent of each category in vowels of consonants, and the use of only one HMM which commonly represents all syllable patterns. The segmentation strategy is to find the optimal HMM sequence. The optimal/best sequence is found by using the O(n) DP matching based on Viterbi algorithm. The concatenated number and boundaries of the best HMM sequence are regarded as the segmentation result. The experimental result on Japanese spoken sentences shows that the rate of segmentation is more than 92% for two male speakers, and the rate is improved to 97.5% by using a duration control mechanism based on a discrete probability distribution.<<ETX>>